Multi-input and multi-output communication method in large-scale antenna system

ABSTRACT

The present invention relates to a multi-input and multi-output communication method in a large-scale antenna system. An MIMO transmission method according to the present invention includes: obtaining statistical channel information on at least one terminal, dividing terminals into a plurality of classes and a plurality of groups based on the statistical channel information, wherein the groups depend on the classes; determining a group beam-forming matrix for each of the divided groups; performing a group beamforming transmission by group based on the group beam-forming matrix to obtain instantaneous channel information; and scheduling terminals based on the instantaneous channel information. Thus, it is possible to decrease the complexity in a scheduling and precoding calculation without an increase in the amount of wireless resources that are required for providing feedback for a reference signal and channel status information.

TECHNICAL FIELD

The present invention relates to a multi-input multi-output (MIMO)communication method in a large-scale antenna system, and moreparticularly, to an MIMO communication method that maximizes up⋅downlinkfrequency efficiency in a correlated large-scale MIMO channelenvironment while requiring feedback of a small amount of channel stateinformation (CSI).

BACKGROUND ART

Due to a drastic increase in data traffic, a Beyond-Fourth-Generation(B4G) mobile communication system requires 10 times an increase infrequency efficiency or more compared to a 4G system such as ThirdGeneration Partnership Project (3GPP) Long Term Evolution (LTE). Asphysical layer techniques necessary to increase frequency efficiency 10times or more as stated above, network MIMO, interference alignment,relay network, heterogeneous network, a large-scale MIMO technique, etc.are currently being mentioned.

The present invention relates to a massive MIMO (or large-scale antenna)system capable of obtaining a very strong effect as a technique forimproving frequency efficiency. Existing large-scale antenna systemshave been limited to a time-division duplex (TDD) scheme. This isbecause a frequency-division duplex (FDD) scheme has a problem that alarge-scale antenna transmitter requires as many reference signals (RSs)and radio resources for CSI feedback as substantially impossible toobtain CSI.

In addition, since the number of users that can be simultaneouslyaccommodated by a large-scale transmitting antenna remarkably increases,there occurs a practical problem in that the complexity of schedulingand precoding calculation becomes very higher than that of an existingsystem.

DISCLOSURE Technical Problem

The present invention is directed to providing a multi-inputmulti-output (MIMO) transmission method capable of reducing thecomplexity of scheduling and precoding calculation even withoutincreasing the amount of radio resources necessary for feedback of areference signal (RS) and channel state information (CSI), andappropriate for a large-scale antenna system.

The present invention is also directed to providing an MIMO receptionmethod capable of reducing the complexity of scheduling and precodingcalculation even without increasing the amount of radio resourcesnecessary for feedback of an RS and CSI, and appropriate for alarge-scale antenna system.

Technical Solution

One aspect of the present invention provides a multi-input multi-output(MIMO) transmission method of a base station in a wireless communicationsystem, the method including: obtaining statistical channel informationon one or more pieces of user equipment (UE); classifying the one ormore pieces of UE into one or more classes and one or more groupssubordinate to the classes on the basis of the statistical channelinformation; determining group beamforming matrices for the respectivedivided groups; performing group beamforming transmission based on thegroup beamforming matrices to the pieces of UE belonging to the groupsaccording to the groups, and obtaining instantaneous channelinformation; and scheduling the pieces of UE on the basis of theinstantaneous channel information, and transmitting data to the piecesof UE on the basis of the scheduling.

Here, the obtaining of the statistical channel information may include:transmitting a channel state information (CSI)-reference signal (RS) tothe one or more pieces of UE; and receiving feedback of the statisticalchannel information measured on the basis of the CSI-RS from the one ormore pieces of UE.

Here, the obtaining of the statistical channel information may includemeasuring the statistical channel information on the basis of soundingRSs (SRSs) received from the one or more pieces of UE.

Here, the statistical channel information may include at least one oftransmit correlation matrices, eigenvalues of the transmit correlationmatrices, eigenvectors of the transmit correlation matrices, anglespreads (ASs), angles of departure (AoDs), and one or more long-termprecoding matrix indicators (PMIs) that mean statistical channelinformation and are selected from a fixed codebook.

Here, the classifying of the one or more pieces of UE may includeclassifying pieces of UE having transmit correlation matrices similar toeach other into one group. Here, the classifying the one or more piecesof UE may include classifying pieces of UE having valid eigenvectors oftransmit correlation matrices similar to each other into one group, andclassifying groups having high orthogonality between valid eigenvectorsof transmit correlation matrices into one class.

Here, the determining of the group beamforming matrices may includedetermining the group-specific group beamforming matrices to bequasi-orthogonal to each other on the basis of the statistical channelinformation and a one-ring channel model. At this time, the groupbeamforming matrices may be determined to be quasi-orthogonal to eachother through block diagonalization (BD).

Here, the obtaining of the instantaneous channel information mayinclude: transmitting CSI-RSs to which the group-specific beamformingmatrices have been applied or CSI-RSs to which the group-specificbeamforming matrices have not been applied to the pieces of UE; andreceiving feedback of the instantaneous channel information measured onthe basis of the CSI-RSs to which the group-specific beamformingmatrices have been applied or the CSI-RS to which the group-specificbeamforming matrices have not been applied from the pieces of UE.

Here, the obtaining of the instantaneous channel information may includemeasuring the instantaneous channel information on the basis of SRSsreceived from the pieces of UE.

Here, the instantaneous channel information may include at least one ofinformation on dominant eigenvector matrices of transmit correlationmatrices, adaptive codebook indices, fixed codebook indices, single userchannel quality indicators (SU-CQIs), and multi-user CQIs (MU-CQIs), andat least one of group interference measurement information and rankinformation (RI). Here, the MIMO transmission method may further includenotifying, at the base station, the pieces of UE of whether to operatein a SU-MIMO mode or a MU-MIMO mode, wherein, when the base station andthe pieces of UE operate in the SU-MIMO mode, the channel informationmay include the SU-CQIs, and when the base station and the pieces of UEoperate in the MU-MIMO mode, the channel information may include atleast one of the MU-CQIs according to the pieces of UE.

Here, the scheduling of the pieces of UE on the basis of theinstantaneous channel information may include scheduling, at the basestation, the pieces of UE belonging to the respective groups and therespective classes independently according to the groups and theclasses.

Another aspect of the present invention provides an MIMO receptionmethod of UE in a wireless communication system, the method including:receiving a signal to which a group beamforming matrix for a groupincluding the UE has been applied; generating instantaneous channelinformation using an RS to which the group beamforming matrix has beenapplied or an RS to which the group beamforming matrix has not beenapplied; and feeding back the instantaneous channel information to abase station.

Here, the MIMO reception method may further include feeding back, at theUE, statistical channel information measured on the basis of a CSI-RSreceived from the base station to the base station, wherein the groupbeamforming matrix may be determined using the statistical channelinformation.

Here, the group beamforming matrix may be determined on the basis of anSRS transmitted by the UE.

Here, the instantaneous channel information may include at least one ofinformation on a dominant eigenvector matrix of a transmit correlationmatrix, an adaptive codebook index, a fixed codebook index, a SU-CQI,and a MU-CQI, and at least one of group interference measurementinformation and RI.

Here, the MIMO reception method may further include notifying, at thebase station, the UE of whether to operate in a SU-MIMO mode or aMU-MIMO mode, wherein, when the base station and the UE operate in theSU-MIMO mode, the instantaneous channel information may include theSU-CQI, and when the base station and the UE operate in the MU-MIMOmode, the instantaneous channel information may include the at least oneMU-CQI according to the UE.

Here, pieces of UE having statistical channel information-based transmitcorrelation matrices similar to each other may be classified into onegroup. Here, pieces of UE having valid eigenvectors of transmitcorrelation matrices similar to each other may be classified into onegroup, and groups having high orthogonality between valid eigenvectorsof transmit correlation matrices may be classified into one class.

Advantageous Effects

In a multi-input multi-output (MIMO) transmission and reception methodaccording to the present invention, pieces of user equipment (UE) areclassified into groups having quasi-orthogonality between themselvesusing similarity between transmit correlation matrices (or channelcovariance matrices) of the pieces of UE, and the groups are caused tooperate as virtual sectors, so that scheduling can be performedindependently according to the groups.

In the present invention, since not all pieces of UE but some pieces ofUE can be independently scheduled according to the aforementionedconcept of virtual sectors (i.e., group-specific independentscheduling), it is possible to remarkably reduce system complexity forperforming multi-user (MU)-MIMO.

Also, in the present invention, group-specific reference signals (GRSs)are introduced, and in practice, it is possible to introduce MU-channelquality indicators (CQIs) through the GRSs, so that MU-MIMO can beeffectively performed.

In addition, when the MIMO transmission and reception method of thepresent invention is used, a specific adaptive codebook rather than afixed codebook may be used for UE (or a UE group), and thus it ispossible to ensure better performance than a fixed codebook such as LongTerm Evolution (LTE).

Furthermore, in the present invention, it is possible to reduce a loadof RSs and UE feedback resources of a frequency-division duplex(FDD)-based large-scale antenna system to a practicable level due toGRSs and an adaptive codebook.

DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram of spatial division among user groups ina multi-input multi-output (MIMO) transmission and reception methodaccording to the present invention.

FIG. 2 is a conceptual diagram showing an example of distribution oflocations of pieces of user equipment (UE) in one sector of a 3-sectorbase station and distribution of radiuses of scatterers.

FIG. 3 is a flowchart illustrating a frequency-division duplex(FDD)-based downlink MIMO transmission and reception method according tothe present invention.

FIG. 4 is a conceptual diagram showing an example of UE grouping in aMIMO transmission and reception method according to the presentinvention.

FIG. 5 is a conceptual diagram of block diagonalization (BD) in a MIMOtransmission and reception method according to the present invention.

FIG. 6 is a conceptual diagram of an example of allocation of channelstate information (CSI) measurement resource or scheduling resourcecandidates according to the present invention.

FIG. 7 is a conceptual diagram of a three-dimensional (3D) beamformingtechnique.

Description of Major Symbols in the Above Figures

-   -   10: Large-scale antenna array    -   10-1 to 10-M: Antenna elements    -   20-1 to 20-K: pieces of UE    -   30-1 to 30-G: Groups

Modes of the Invention

While the present invention can be modified in various ways and take onvarious alternative forms, specific embodiments thereof are shown in thedrawings and described in detail below as examples.

However, there is no intent to limit the present invention to theparticular forms disclosed. On the contrary, the present invention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the appended claims.

The terminology used herein to describe embodiments of the invention isnot intended to limit the scope of the invention. Singular forms includeplural forms unless the context clearly indicates otherwise. It will befurther understood that the term “comprises,” “comprising,” “includes,”or “including,” when used herein, specifies the presence of statedfeatures, integers, steps, operations, elements, components, or groupsthereof, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components, orgroups thereof.

Unless otherwise defined, all terms including technical and scientificterms used herein are to be interpreted as is customary in the art towhich the present invention belongs. It will be further understood thatterms as those defined in a generally used dictionary are to beinterpreted as having meanings in accordance with the meanings in thecontext of the relevant art and not in an idealized or overly formalsense unless clearly so defined herein.

The term “user equipment (UE)” used herein may be referred to as amobile station (MS), user terminal (UT), wireless terminal, accessterminal (AT), terminal, subscriber unit, subscriber station (SS),wireless device, wireless communication device, wirelesstransmit/receive unit (WTRU), mobile node, mobile, or other terms.Various embodiments of UE may include a cellular phone, a smart phonehaving a wireless communication function, a personal digital assistant(PDA) having a wireless communication function, a wireless modem, aportable computer having a wireless communication function, aphotographing apparatus such as a digital camera having a wirelesscommunication function, a gaming apparatus having a wirelesscommunication function, a music storing and playing appliance having awireless communication function, an Internet home appliance capable ofwireless Internet access and browsing, and also portable units or UEhaving a combination of such functions, but are not limited to these.

The term “base station” used herein generally denotes a fixed or movingpoint that communicates with UE, and may be a common name for Node-B,evolved Node-B (eNode-B), base transceiver system (BTS), access point,relay, femto-cell, and so on.

Hereinafter, exemplary embodiments of the present invention will bedescribed in detail with reference to the appended drawings. To aid inunderstanding the present invention, like numbers refer to like elementsthroughout the description of the figures, and the description of thesame element will not be reiterated.

Summary of MIMO Transmission and Reception Method According to PresentInvention

A multi-input multi-output (MIMO) transmission and reception methodaccording to the present invention is applied to the uplink and downlinkof cellular communication.

In description below, it is assumed that one cell consists of a basestation having M antennas and K users (pieces of UE) each having Nantennas, and transmitting antenna correlation of each piece of UE ishigh (i.e., an angle spread (AS) is small). For example, in a channelenvironment in which downlink urban macro and line of sight (LOS)components are strong, transmitting antenna correlation is high.

For convenience, it is assumed that the K users can be classified into Ggroups that can be spatially separated according to similarity oftransmitting antenna correlation, and each group includes K′ users. Forconvenience, it is assumed that all the groups consist of the samenumber of users.

A channel model taken into consideration in the present invention isEquation 1 below.

H=R _(R) ^(1/2) H _(W) R _(T) ^(1/2)  [Equation 1]

Here, H_(W) is an independently and identically distributed (i.i.d.)channel matrix, R_(T) is a transmit correlation matrix, and R_(R) is areceive correlation matrix. For convenience, a so-called one-ringchannel model is assumed in multi-user (MU)-MIMO, and it is assumed thatR_(R)=I, that is, there is no receive correlation.

A transmission signal model proposed by the present invention isEquation 2 below.

x=BPd  [Equation 2]

Here, B is a beamforming matrix based on statistical characteristics ofa channel, P is a precoding matrix based on channel information {tildeover (H)}=HB , and d is a data symbol vector.

A reception signal model proposed by the present invention is Equation 3below.

y=HBPd+z  [Equation 3]

Here, Z denotes a noise signal, and HB can be presented by Equation 4below.

         [Equation  4] ${HB} = {\begin{bmatrix}{H_{1}B_{1}} & {H_{1}B_{2}} & \ldots & {H_{1}B_{G}} \\{H_{2}B_{1}} & {H_{2}B_{2}} & \ldots & {H_{2}B_{G}} \\\vdots & \vdots & \ddots & \vdots \\{H_{G}B_{1}} & {H_{G}B_{2}} & \ldots & {H_{G}B_{G}}\end{bmatrix} \approx \begin{bmatrix}{H_{1}B_{1}} & 0 & \ldots & 0 \\0 & {H_{2}B_{2}} & \ldots & 0 \\\vdots & \vdots & \ddots & \vdots \\0 & 0 & \ldots & {H_{G}B_{G}}\end{bmatrix}}$

Here, H_(g) is an overall channel matrix of group g, and B_(g) ∈C^(M×b)is a beamforming matrix of group g. In Equation 4 above, the approximateequality sign corresponds to a case in which the condition of Equation 5below is satisfied.

H _(m) B _(n)=0,m≠n  [Equation 5]

Then, P=diag(P₁, . . . ,P_(G)).

The core of the MIMO transmission and reception method proposed in thepresent invention is to set B_(g) to satisfy the condition and scheduleusers having such a beamforming matrix for the same time.

FIG. 1 is a conceptual diagram of spatial division among user groups ina MIMO transmission and reception method according to the presentinvention.

Referring to FIG. 1, a base station has a large-scale antenna array 10consisting of M antenna elements 10-1, 10-2, . . . , and 10-M. There areK pieces of active UE 20-1, 20-2, . . . , and 20-K, and the K pieces ofactive UE are classified into G groups. For example, a first group 30-1includes the first piece of UE 20-1 and the second piece of UE 20-2, anda second group 30-2 includes the third piece of UE 20-3. A G^(th) group30-G includes the (K-1)^(th) piece of UE 20-(K-1) and the K^(th) pieceof UE 20-K.

Here, a channel matrix of the first group 30-1 consisting of the firstpiece of UE 20-1 and the second piece of UE 20-2 corresponds to H₁ .

Next, description will be made regarding dimensional reduction of aninstantaneous channel matrix that can be obtained using the MIMOtransmission and reception method proposed in the present invention.First, it is assumed that K users are indexed as Equation 6 below topresent group indices. g_(k) denotes an index of a k^(th) piece of UE ingroup g.

g _(k)=(g−1)×K′+k,g=1, . . . G,k=1, . . . ,K′  [Equation 6]

Meanwhile, it is effective to use a receive beamforming or combiningmatrix of a receiver when receive correlation is high. Then, it ispossible to know that dimensions of an instantaneous channel matrix tobe fed back to a base station by a user according to the transmissionand reception method of the present invention is reduced as presented inEquation 7 below.

H _(gk) :N×M⇒{tilde over (H)}_(gk) =C _(gk) H _(gk) B _(gk):c×b  [Equation 7]

Here, C_(gk) ∈C^(c×N) is a receive combining matrix of a user g_(k) ,and N≥c, M≥b. In particular, since it is expected that a case ofN>>c,M>>b will frequently occur in a large-scale antenna system, it ispossible to remarkably reduce dimensions of an instantaneous channelmatrix {tilde over (H)}_(g) _(k) that a user should feed back to a basestation. In addition, each group performs precoding on the basis of{tilde over (H)}_(g), the complexity of precoding matrix calculationalso is significantly reduced. It is possible to know that the effectsare obtained in a single user (SU)-MIMO system as well.

Next, assuming for convenience that users of group g have the sametransmit correlation matrix, various forms that B_(g) has according tostatistical information and an antenna arrangement given to a basestation will be described.

A. Case of Base Station Knowing Transmit Correlation Matrix Informationon Each Group

Eigenvectors of a transmit correlation matrix of group g can be fed backfrom a user or estimated using an uplink pilot signal. A transmitcorrelation matrix is statistical information, and thus a base stationmay receive feedback of the corresponding information at enough timeintervals. In this case, it is possible to obtain B_(g) in a variety ofthe following forms.

B_(g) may be a matrix consisting of as many eigenvectors as the numberof meaningful ranks of the transmit correlation matrix of group g.

When L subarrays are arranged at enough intervals as a large-scaleantenna array, the transmit correlation matrix of group g becomes ablock diagonal matrix. In this case, channel information that each usershould feed back becomes an eigenvector of a block matrix disposed on adiagonal, and thus a load of feedback can be remarkably reduced. Adistributed antenna system can be understood as corresponding to aspecial case of the large-scale antenna array divided into the Lsubarrays. When a practical rank (i.e., a rank excluding too smalleigenvalues) of the transmit correlation matrix of group g is large, inorder to make transmit correlation matrices of groups orthogonal to eachother, a sufficient dimension of H_(g)B_(g) matrix can be ensured byreducing the number of groups, and then inter-group interference can beremoved through block diagonalization (BD). Alternatively, it ispossible to control inter-group interference by designing B_(g) of eachgroup to have b that is smaller than a meaningful rank of the transmitcorrelation matrix of group g.

B. Case of Base Station Not Knowing Transmit Correlation MatrixInformation on Each Group

It is possible to have B_(g) in the following form.

Virtual sectors can be made using predetermined fixed beamforming tospatially separate user groups. Here, an example of fixed beamformingcan be the Third Generation Partnership Project (3GPP) Long TermEvolution (LTE) Rel. 10 codebook based on unitary beamforming, a usercan feed back a single beam index and a plurality of beam indices ofstrong signals among received beams to a base station, and the basestation should be able to appropriately perform scheduling using thecorresponding information so that there is little interference betweenuser groups.

The user may feedback a transmission angle spread (AS) and angle ofdeparture (AoD) extracted from a transmit correlation matrix to the basestation.

The core of scheduling proposed in the present invention is to make HBas close to a block diagonal matrix as possible. Thus, the schedulinggenerally includes two steps.

In a first step, groups are made using information such as eigenvectorsor beam indices of transmit correlation matrices of all users, so thatHB becomes a block diagonal matrix. For this reason, the respectivegroups independently perform intra-group scheduling without causingsignificant interference to each other. At this time, a base station mayhave to signal beamforming matrices of the respective groups orrespective users to the users.

In a second step, scheduling is performed using an instantaneous channelmatrix {tilde over (H)}_(gk) fed back by users in a group, and spatialmultiplexing is performed through precoding.

Due to the aforementioned two-stage scheduling, the complexity ofscheduling and precoding calculation of a system can be remarkablyreduced.

Downlink pilots can have two forms.

A first form is a pilot in a general form transmitted in all directionsof a sector. For example, the first form may be in accordance with apilot signal having the same structure as 3GPP LTE.

A second form is a pilot signal multiplied by a beamforming matrix. Incase of fixed beamforming, the second form is a pilot form necessary fora user to transmit a beam index.

A large-scale MIMO uplink has a problem in that a dimension of areception channel matrix is large, and the calculation complexity of areceiving algorithm exponentially increases. The present invention showsthat an uplink MU-MIMO reception method for solving this problem can beobtained by applying the principle of the above-described downlink MUtransmission method to a reception method. In other words, since a basestation knows all channel information through an uplink pilot, it ispossible to significantly reduce dimensions of a reception vector ofeach group and lower the calculation complexity of a receiving algorithmto a practicable level when reception beamforming is performed to removeinter-group interference through an appropriate scheduling.

FDD-Based Downlink MIMO Transmission and Reception Method According toPresent Invention

FIG. 2 is a conceptual diagram showing an example of distribution oflocations of pieces of UE in one sector of a 3-sector base station anddistribution of radiuses of scatterers. The present invention will bedescribed below with reference to FIG. 2 in parallel.

A) Entire Procedure

FIG. 3 is a flowchart illustrating an FDD-based downlink MIMOtransmission and reception method according to the present invention.

Referring to FIG. 3, an FDD-based downlink MIMO transmission andreception method according to the present invention is a MIMOtransmission method of a base station in a wireless communicationsystem, and may include: a step of obtaining statistical channelinformation on one or more pieces of UE (S310); a step of classifyingthe one or more pieces of UE into one or more classes and one or moregroups subordinate to the classes on the basis of the statisticalchannel information (S320); a step of determining group beamformingmatrices for the respective divided groups (S330); a step of performinggroup beamforming transmission based on the group beamforming matricesto the pieces of UE belonging to the groups according to the groups, andobtaining instantaneous channel information (S340); and a step ofscheduling the pieces of UE on the basis of the instantaneous channelinformation and transmitting data to the pieces of UE on the basis ofthe scheduling (S350).

The respective steps will be described in brief below, and operation andelements constituting each step will be described later in sections B)to F). Also, a fixed codebook-based procedure and an adaptivecodebook-procedure will be described later. In step S310, a base stationcan receive feedback of statistical channel information from one or morepieces of UE or measure the statistical channel information through anuplink sounding reference signal (SRS). The statistical channelinformation can include at least one of transmit correlation matrices,eigenvalues of the transmit correlation matrices, eigenvectors of thetransmit correlation matrices, ASs, AoDs, and one or more long-termprecoding matrix indicators (PMIs) that mean statistical channelinformation and are selected from a fixed codebook.

Statistical channel information can be obtained when a base station setsand transmits a channel state information (CSI)-reference signal (RS) tothe pieces of UE and receives feedback of results measured through thereceived CSI-RS, or can be measured by the base station through anuplink SRS transmitted by UE. Respective pieces of information includedin the statistical channel information will be described later.

In step S320, the base station can classify the one or more pieces of UEinto one or more classes and one or more groups subordinate to theclasses on the basis of the statistical channel information. Accordingto a procedure selected from a fixed codebook-based procedure and anadaptive codebook-based procedure, step S320 can be configureddifferently. For example, according to the fixed codebook-basedprocedure, the pieces of UE feed back long-term PMIs selected from afixed codebook as statistical channel information, which means that thepieces of UE designate classes and groups to which the pieces of UEthemselves will belong in the first instance. At this time, the basestation may ignore the class and the group selected by the pieces of UE,select optimum classes and groups in the second instance, and notify thepieces of UE of the selected optimum classes and groups. The detailedprocedure based on a fixed codebook will be described later. Also,statistical channel information and group/class classification will bedescribed later in section B).

In step S330, group beamforming matrices for the respective dividedgroups are determined.

At this time, in the fixed codebook-based procedure, group beamformingmatrices are selected from among previously generated group beamformingmatrices. On the other hand, in the adaptive codebook-based procedure,group beamforming matrices are generated on the basis of the receivedstatistical channel information. Generation of group beamformingmatrices will be described later in section C).

In step S340, the base station performs group beamforming transmissionbased on the group beamforming matrices to the pieces of UE belonging tothe groups according to the groups. The base station can receivefeedback of instantaneous channel information measured from CSI-RSsignals to which group beamforming has been applied or CSI-RS signals towhich group beamforming has not been applied, or can measure theinstantaneous channel information through SRSs received from the piecesof UE. RSs of the present invention will be described later in sectionD).

Here, the instantaneous channel information can be fed back to the basestation using an implicit feedback scheme or an explicit feedbackscheme.

The instantaneous channel information may include at least one ofinformation on dominant eigenvector matrices of the transmit correlationmatrices, adaptive codebook indices, fixed codebook indices, SU-channelquality indicators (SU-CQIs), and MU-CQIs, and at least one of groupinterference measurement information and rank information (RI).

A detailed feedback method of instantaneous channel information will bedescribed later with the fixed codebook-based procedure and the adaptivecodebook-based procedure.

Lastly, in step S350, the base station selects pieces of UE to serviceaccording to the respective groups through instantaneous channelinformation fed back from the pieces of UE and a scheduling algorithm,and transmits a control signal and data.

At this time, the base station can transmit demodulation group-specificRSs (DM-GRSs) to which group-specific beamforming matrices are appliedto the pieces of UE with the data, and the pieces of UE can demodulatethe data using the DM-GRSs.

In the FDD-based downlink MIMO transmission and reception methodaccording to the present invention, a fixed codebook-based MIMOtransmission method of a base station and an adaptive codebook-basedMIMO transmission method of a base station will be described in furtherdetail below. Although the MIMO transmission methods are described fromthe viewpoint of a base station, MIMO reception methods of UEcorresponding to the MIMO transmission methods can also be described byinference.

First, the fixed codebook-based procedure will be described.

An example of operation based on a fixed codebook of the FDD-baseddownlink MIMO transmission method according to the present invention caninclude a step of transmitting a CSI-RS (1-1), a step of receivinginformation indicating a class and a group determined through the CSI-RSand to which each of one or more pieces of UE belongs from the piece ofUE (1-2), a step of notifying the pieces of UE of the classes and thegroups of the pieces of UE determined on the basis of the information(1-3), a step of generating or selecting group-specific beamformingmatrices on the basis of the determined classes and groups (1-4), a stepof transmitting CSI-RSs to which the group-specific beamforming matricesare applied to the respective groups (1-5), a step of receiving channelinformation measured on the basis of the CSI-RSs to which thegroup-specific beamforming matrices are applied from the pieces of UE(1-6), and a step of scheduling the pieces of UE on the basis of thechannel information, and transmitting data to the pieces of UE on thebasis of the scheduling (1-7). Description will be made below under theassumption that channel information feedback from UE is implicit channelfeedback.

The respective steps will be described in further detail below.

In step 1-1, a base station sets and transmits a general CSI-RS topieces of UE. Here, the general CSI-RS may denote a CSI-RS to which nogroup-specific beamforming matrix to be described later has not beenapplied.

In step 1-2, the pieces of UE select optimum classes and groups on thebasis of the CSI-RS transmitted by the base station, and feed backinformation indicating the selected classes and groups to the basestation. The information indicating the classes and groups can beconfigured to indicate one or more classes and groups. In step S310, thepieces of UE can select B_(s) ^((t)) that maximizes ∥h_(i) ^(H)B_(g)^((t))∥ or the average

$\frac{1}{N}{\sum\limits_{n = 1}^{N}\; {{{h_{i}^{H}(n)}{B_{g}^{(t)}(n)}}}}$

of N slots as a long-term PMI using the CSI-RS transmitted by the basestation, and feed back the long-term PMI to the base station asinformation indicating classes and groups. The long-term PMI denotesstatistical channel information on UE. Since a class and a group of UEchange very slowly according to movement of the UE, the long-term PMImay be fed back for a very long term or fed back when the long-term PMIexceeds a specific threshold value, that is, only when there is achange. When p long-term PMIs are fed back, for example, p long-termPMIs in decreasing order of the selection reference values are fed back.

Meanwhile, in order for UE to transmit information indicating a classand a group as a long-term PMI, the base station can provide informationon a used fixed codebook to the UE. For example, when various fixedcodebooks are used according to the number and a pattern of antennas ofthe base station and a type (urban/rural and macro/micro) of the basestation, the base station can provide information on the fixed codebooksin use to the UE. Such information on a fixed codebook can betransferred to the UE, for example, using a physical broadcast channel(PBCH).

In step 1-3, the base station classifies the pieces of UE according toclasses and groups on the basis of the information fed back from thepieces of UE. In this process, classes and groups different from theclasses and groups reported by the pieces of UE in step 1-2 can beassigned to the pieces of UE through class and group rearrangement ofthe base station. Thus, when the pieces of UE have fed back informationindicating several classes and groups in step 1-2, or classes and groupsto which the pieces of UE belong are changed through class rearrangementof the base station, the base station notifies the corresponding piecesof UE of the determined classes and groups of the pieces of UE through acontrol signal.

In step 1-4, the base station generates or selects group-specificbeamforming matrices on the basis of the determined classes and groups.At this time, the base station may generate optimum beamforming matricesfor the respective classified groups, or select optimum beamformingmatrices from among previously generated beamforming matrices accordingto the groups. In step 1-5, the base station transmits CSI-RSs to whichthe group-specific beamforming matrices are applied to the respectivegroups.

In step 1-6, the pieces of UE measure channel information using theCSI-RSs transmitted by the base station and to which the group-specificbeamforming matrices are applied, and report the channel information tothe base station. Here, the channel information can include at least oneof SU-CQIs and MU-CQIs, short-term PMIs, and rank indicators (RIs).

A MU-CQI is calculated as a self-signal-to-interference plus noise ratio(SINR). Here, interference within the same group and interference fromother groups all are calculated and reflected in an interference signal.Basically, the pieces of UE calculate interference assuming that allbeams of all the groups are used. This is possible because the pieces ofUE know their channels and beams B_(g) ^((t)) of all the groups.Meanwhile, when the number of users in a cell is small, a control signalis necessary for the base station to reduce the number of beams used byeach group and notify the pieces of UE of the corresponding beamindices. At this time, the base station notifies the pieces of UE in thecorresponding groups of group-specific used beams, so that the pieces ofUE accurately estimate interference of other groups and calculateMU-CQIs. Also, since the pieces of UE simultaneously feed back SU-CQIsand MU-CQIs, the base station may dynamically select and schedule thepieces of UE, or notify the pieces of UE of whether to operate in aSU-MIMO mode or a MU-MIMO mode using a control signal, so that thepieces of UE feed back the SU-CQIs or one or more MU-CQIs according tothe respective pieces of UE.

Lastly, in step 1-7, the base station finds an optimum UE combination onthe basis of the channel information, schedules the pieces of UE, andtransmits data to the pieces of UE on the basis of the scheduling.

When a base station performs optimum scheduling on the basis of MU-CQIsof UE, selected pieces of UE have little inter-group interference. Thus,pieces of UE selected from different groups can demodulate their datathrough DM-GRSs that are quasi-orthogonal to each other and use the sameresources. Interference between different groups can be additionallyreduced using a quasi-orthogonal sequence, and interference betweendifferent users in the same group can be removed using an orthogonalsequence.

Meanwhile, a design standard for generating a fixed codebook consistingof the long-term PMI mentioned in step 1-2 and the short-term PMImentioned in step 1-6 is as follows. First, description will be madeunder the assumption of a co-polarization antenna.

A fixed codebook consists of T classes and G groups belonging to eachclass.

The respective classes may have different number of groups. A matrixU_(g) ^((t)) forming eigenvector spaces of groups constituting one classis made using the one-ring channel model. Parameters necessary to thisend are ASs and AoDs of the groups. An AS of a group is determined by ASdistribution of UE in a cell, each group AoD is determinedcomprehensively according to the following two standards.

Eigenvector spaces of respective groups should be as orthogonal aspossible. This is intended to reduce inter-group interference.

AoDs of respective groups should be disposed as equally as possible.This is intended to minimize a disagreement between an eigenvector spaceof UE and an eigenvector space of a group to which the UE belongs.

A BD scheme is applied to class-specific U_(g) ^((t)) sets obtainedusing the aforementioned method, and thereby a beamforming matrix B_(g)^((t)) constituting a codebook is generated. In this way, inter-groupinterference can be further reduced. Here, the long-term PMI denotes anindex (t, g) of B_(g) ^((t)), and the short-term PMI denotes one or aplurality of column vectors in B_(g) ^((t)) corresponding to UEaccording to a transmission rank (RI).

The codebook has been described above under the assumption of aco-polarization antenna. On the other hand, in case of across-polarization antenna, two polarization antenna arrays areindependently processed to have M/2 beam vectors per one polarity, andit is possible to configure a long-term PMI and a short-term PMI in thesame way as described above. Also, like in the LTE scheme, a co-phasingparameter can be prepared to induce constructive combining between beamsof two polarization antennas. However, in this case, UE shouldaccurately estimate at least interference of other users in a group tocalculate a MU-CQI. To this end, the UE calculates a plurality ofMU-CQIs according to the number of cases of co-phasing parameters of theother users in the group, and feeds back co-phasing parameters to therespective users, so that a base station schedules users in the group.At this time, the UE can reduce the number of feedback bits bytransmitting a first MU-CQI and only offsets of the other MU-CQIs withrespect to the first MU-CQI.

The above procedure has been described on the basis of an existingCSI-RS. Meanwhile, an existing CSI-RS can be replaced by a long-termCSI-RS and a short-term CSI-GRS. Here, the CSI-GRS is a GRS beamformedusing B_(g) ^((t)) and is transmitted by sharing the same resourcesbetween groups, thereby reducing consumption of RS resources. Thelong-term CSI-RS is intended for UE to estimate and feed back along-term PMI, and the short-term CSI-GRS is intended to feed back ashort-term PMI.

Next, the adaptive codebook-based procedure will be described.

An example of operation based on an adaptive codebook of the FDD-baseddownlink MIMO transmission method according to the present invention caninclude: a step of obtaining statistical channel information on one ormore pieces of UE (2-1); a step of classifying the one or more pieces ofUE into classes and groups on the basis of the statistical channelinformation, and generating group-specific beamforming matrices (2-2); astep of transmitting CSI-RSs to which the group-specific beamformingmatrices are applied to the respective groups (2-3); a step of receivingchannel information measured on the basis of the CSI-RSs to which thegroup-specific beamforming matrices are applied from the pieces of UE(2-4); and a step of scheduling the pieces of UE on the basis of thechannel information, and transmitting data to the pieces of UE on thebasis of the scheduling (2-5).

The respective steps will be described in further detail below.

In step 2-1, a base station obtains statistical channel information onone or more pieces of UE. At this time, the base station can measure thestatistical channel information using SRSs transmitted by the pieces ofUE, or transmit long-term CSI-RSs to the pieces of UE and receivefeedback of the statistical channel information measured by the piecesof UE. An example of the statistical channel information may beeigenvector matrices of the pieces of UE. Alternatively, another exampleof the statistical channel information may be ASs and AoDs of the piecesof UE.

In step 2-2, the base station classifies the pieces of UE according toclasses and groups using the statistical channel information obtained instep 2-1, and can generate optimal beamforming matrices B_(g) ^((t)) forthe respective groups. Such B_(g) ^((t)) constitute an adaptive codebookthat changes very slowly along with movement of the pieces of UE.

In step 2-3, the base station sets CSI-GRSs that are beamformed usingthe beamforming matrices generated in step 2-2, and broadcasts theCSI-GRSs. Here, the CSI-GRSs are GRSs beamformed using B_(g) ^((t)) andtransmitted by sharing the same resources between groups, therebyreducing consumption of RS resources.

In step 2-4, the pieces of UE measure channel information using theCSI-RSs transmitted by the base station and to which the group-specificbeamforming matrices are applied, and report the channel information tothe base station. Here, the channel information can include at least oneof SU-CQIs and MU-CQIs, short-term PMIs, and RIs.

Here, a method of determining the MU-CQIs in the channel information anda method of signaling the channel information are the same as those incase of a fixed codebook described above, and detailed descriptionthereof will be omitted.

Lastly, in step 2-5, the base station finds an optimum UE combination onthe basis of the channel information, schedules the pieces of UE, andtransmits data to the pieces of UE on the basis of the scheduling.

When a base station performs optimum scheduling on the basis of MU-CQIsof UE, selected pieces of UE have little inter-group interference. Thus,pieces of UE selected from different groups can demodulate their datathrough DM-GRSs that are quasi-orthogonal to each other and use the sameresources. Interference between different groups can be additionallyreduced using a quasi-orthogonal sequence, and interference betweendifferent users in the same group can be removed using an orthogonalsequence.

To reduce resource consumption, the aforementioned CSI-GRS and DM-GRS donot use separate resources, but rather may be combined into one GRS andused.

In other words, the DM-GRS may serve as the CSI-GRS. In this case, whenthe UE is instructed to operate in the SU-MIMO mode, it is possible todemodulate its physical downlink shared channel (PDSCH) through the GRS.To solve this problem, the base station transmits quasi-orthogonalDM-RSs based on SU-CQIs using separate resources in subframes in whichthe GRS for CSI is transmitted at intervals of, for example, 5 ms, sothat the UE demodulates the PDSCH.

B) UE Grouping 1) Statistical Channel Information

This paragraph describes UE grouping according to statistical channelinformation that is a core step of the above-described MIMO transmissionand reception method according to the present invention. First, a basestation receives feedback of statistical channel information through aCSI-RS, or obtains the statistical channel information through an uplinkSRS. The statistical channel information can have the following forms.

Transmit correlation matrix or channel covariance matrix

Valid eigenvalue and eigenvector of transmit correlation matrix

AS and AoD

Long-term PMI denoting statistical channel information on UE

A transmit correlation matrix that is a statistical characteristic of aUE channel is a statistical value that changes very slowly along withmovement of UE.

This is because a scatterer environment changes only when UE moves.Also, MU-MIMO is generally used by low-speed UE only. The simplest formof a transmit correlation matrix estimation scheme isR_(gk)=E[h_(gk)h_(gk) ^(H)] in which the one-ring channel model isassumed.

2) UE Grouping Procedure

A summary of UE grouping is as follows. A base station classifies piecesof UE g_(k) having similar valid eigenvector (an eigenvectorcorresponding to a valid eigenvalue) matrices U_(gk) into one group,thereby creating a plurality of groups. Also, groups having highorthogonality between their eigenvectors constitute one class.

While classes that are classified in this way use differenttime/frequency resources, groups in one class are allocated the sametime/frequency resources. The number of classes is referred to as T.

A method of finding groups having high orthogonality between theireigenvectors can be implemented in several ways. An eigenvector matrixU_(g) of a group having high orthogonality should satisfy a relationshipshown in Equation 8 below with an eigenvector matrix U_(g), of anothergroup.

U _(g) ^(H) U _(g′),≈0,∀g′≠g  [Equation 8]

In this paragraph, a simple U_(g) calculation method is proposed. First,in consideration of eigenvectors of pieces of UE or distribution of ASsand AoDs, the base station estimates the number of classes and thenumber of groups and reference angles of the respective classes. It ispossible to calculate AoDs of G-1 beam vectors orthogonal to thereference angles (i.e., a total of G orthogonal beam vectors), grouptransmit correlation matrices are calculated according to a formula ofthe one-ring channel model, and the corresponding group eigenvectormatrices U_(g) are generated through singular value decomposition (SVD).

The base station measures similarity between the given class-specificgroup eigenvector matrices and eigenvector matrices U_(i) of therespective pieces UE through inner products of them, determines groupsof the most similar classes, and classifies the pieces of UE into thecorresponding groups. Similarity between an eigenvector of a UEcorrelation matrix and that of a group correlation matrix can be definedas Equation 9 below.

∥U_(i) ^(H) U _(g)∥≤α₀ , i∈[1:K]  [Equation 9]

Here,

is a Frobenius norm.

Here, group classification is rearranged by adjusting the initial numberof classes and the initial number of groups and a reference angle of aclass whose similarity value does not satisfy a reference value α⁰, sothat the similarity value satisfies the reference value. Such groupclassification changes very slowly, and thus an increase in calculationcomplexity caused by the change will be limited.

FIG. 4 is a conceptual diagram showing an example of UE grouping in aMIMO transmission and reception method according to the presentinvention.

FIG. 4 shows how pieces of UE can be grouped according to the presentinvention in the example of distribution of UE and scatterers shown inFIG. 2 above.

Referring to FIG. 4, circles drawn with dotted lines denote locations ofpieces of UE classified into class 1 and circles of scatterers, andcircles drawn with solid lines denote pieces of UE of class 2. Class 1consists of four groups, and class 2 consists of three groups. Dottedstraight lines and solid straight lines denote reference angles used forgenerating orthogonal beams of the respective groups.

Thus, it is possible to see a result of UE grouping performed so thatthere are the circles of pieces of UE belonging to each class with theircenters located around the corresponding dotted or solid straight line.Also, it is possible to see that two circles filled with diagonal linesconstitute one group in class 1.

When the total number of pieces of UE is not enough, the number ofpieces of UE per class decreases, and the number of pieces of UE pergroup also decreases in proportion to the decrease. This results indeterioration of the frequency efficiency of the MIMO transmission andreception method according to the present invention, but on secondthoughts, a small number of pieces of UE in a cell implies little systemload, which means that there is no problem in service even with lowfrequency efficiency.

In addition, since large-scale antenna MIMO technology such as thepresent invention is intended to simultaneously provide service to manyusers using the same resources and to improve quality of experience(QoE) by causing a system to bear overload at peak time, it may beassumed that the number of pieces of UE is about ten times a number s oflayers simultaneously served using the same resources. Furthermore, incase of multi-antenna UE, respective antennas may be regarded asseparate users and scheduled, and thus the assumption about s isrealistic.

The UE grouping has been described above assuming a case in which raysof a transmission signal transmitted to UE spreads as much as an AS.Meanwhile, when a scatterer such as a skyscraper is around a basestation, or in case of a micro cell base station, rays of a transmissionsignal may be transmitted with two or more AoDs and ASs. In this case,the corresponding UE belongs to two or more groups, is classified intothe groups and managed, and performs UE feedback necessary for therespective groups. In addition, the UE grouping described above is underthe assumption that a base station knows statistical channel informationon all pieces of UE. However, when the assumption is not satisfied, thebase station requires feedback of statistical channel information fromthe pieces of UE for UE grouping. For example, in a method in which thebase station transmits a long-term CSI-RS and the pieces of UE feed backstatistical channel information on the basis of a fixed codebook orestimate and feed back ASs and AoDs, the base station obtains thestatistical channel information on the pieces of UE. The base stationperforms the above-described UE grouping with reference to the UEfeedback.

At this time, by determination of the base station, UE may not beclassified into a group corresponding to an AoD fed back by the UEitself, but may be classified into another class and group that aresystematically more appropriate. Thus, in this case, the base stationshould notify the UE of the class and group to which the UE belongs.

C) Group Beamfoming Matrix

1) Generation of Group Beamforming Matrix

A beamforming matrix of the corresponding group is generated from auniversal set or a subset of a group eigenvector matrix U_(g) selectedthrough the above-described UE grouping. In other words, when a rank (ora column size) of the group beamforming matrix is made to be the same asr* of the group eigenvector matrix, the group beamforming matrix is asEquation 10 below.

B _(g) =U _(g)  [Equation 10]

Such group beamforming matrices become quasi-orthogonal to each otherthrough group classification and satisfy Equation 8. Eventually, groupbeamforming matrices satisfy Equation 8 and enable the present inventionto perform large-scale MU-MIMO while minimizing a load of RSs and UEfeedback resources.

The aforementioned generation of a basic group beamforming matrix B_(g)satisfies Equation 8 under the assumption that the above-described UEgrouping is appropriately performed and group eigenvectors U_(g) arequasi-orthogonal to each other. However, when the number of activepieces of UE in a base station is small and it is impossible to make anumber T of classes large, the UE grouping alone may not always generategroups that are quasi-orthogonal to each other. In this case, the basestation can forcibly make groups that are not orthogonal to beorthogonal through a BD scheme, which will be described later.

2) Block Diagonalization

FIG. 5 is a conceptual diagram of BD in a MIMO transmission andreception method according to the present invention.

With reference to FIG. 5, BD for a specific group g will be conceptuallydescribed.

In a vector space of a total of M dimensions, eigenvectors of G-1 groupsother than group g form a subspace (an ellipse 510 in the drawing) ofr*(G-1) dimensions, which becomes interference of the other groupsexerted on group g. Then, a null space (a line 501) orthogonal to thesubspace is made, and a subspace (a line 503) formed by eigenvectors ofa self-signal of r* dimensions is projected to the null space. It ispossible to know that the projected self-signal subspace (a line 502) isorthogonal to the interference subspace.

In the next step, an eigenvector in the projected self-signal subspaceis calculated. The eigenvector forms an optimum beamforming matrix(which corresponds to eigen-beamforming causing slight distortion, andthus can be referred to as being close to the optimum in an implicitchannel feedback-based MU-MIMO beamforming scheme) in a self-subspace towhich the eigenvector is projected while orthogonality with eigenvectorsof the other groups is maintained.

The only condition enabling the above-described BD is roughly

$r^{*} \leq {\frac{M}{G}.}$

Also, estimation is made in advance so that as many groups as possiblebecome orthogonal to each other through UE grouping. For this reason, avery small part of all groups interfere with each other, and BD isperformed on the corresponding groups only. Thus, it is possible tominimize loss caused by BD.

In this specification of the present invention, implicit UE feedback ismainly handled. Thus, when a number K of active pieces of UE in a systemis large enough, s′=b′, and P_(g) is as Equation 11 below.

P _(g) =I, ∀ _(g)  [Equation 11]

Meanwhile, a case in which s′<b′ due to a small number of active piecesof UE or determination of a scheduler is as follows. It is assumed thata set of all active users corresponding to group g is

={1,2, . . . ,K′}, and an index set of a maximum of b′ usablegeneralized beamforming (GBF) vectors is {1, 2, . . . , b′}. A basestation scheduler maps actually scheduled s′ users in

to a subset

∈{1,2, . . . , b′}. Thus, in this case, P_(g) is presented as follows.

P _(g)=[

₍₁₎, . . . ,

_((s′)])  [Equation 12]

Here, e_(n) is a b′ dimensional column vector whose n^(th) element aloneis 1 and whose other elements are 0, and

denotes an i^(th) element of a subset

..

Consequently, when BD is not used, beamforming according to the presentinvention corresponds to eigen-beamforming as Equation 10, and Equation2 is presented as Equation 13 below.

x _(g) =B _(g) P _(g) d _(g) , ∀g  [Equation 13]

Here, d_(g) is a data symbol vector of the user set

selected by the scheduler, and P_(g) is presented as Equation 11 orEquation 12.

P_(g) has been described above under the assumption of MU-MIMO. In caseof SU-MIMO, P_(g) can be a co-phasing factor such as a dual codebook ofLTE, and also a more detailed precoding matrix.

D) Reference Signal (RS)

1) CSI-RS

A CSI-RS in the present invention exists for feedback of statisticalchannel information, and an RS for feedback of instantaneous channelinformation is a GRS to be described below. Thus, when a base stationcan obtain enough statistical channel information through an uplink SRS,no CSI-RS is necessary in the present invention.

If it is difficult to estimate statistical channel information using anSRS only, when a maximum number M of transmitting antennas is consideredto be 64 and there will be no cell-specific RS (CRS) transmission of LTEin the future, resources used for CRS transmission will be able to beused for CSI-RS transmission.

Such a CSI-RS is for UE to estimate and feed back statistical channelinformation rather than instantaneous channel information, and may betransmitted for a much longer period than an existing CSI-RS.

2) Group-Specific RS (GRS)

GRSs proposed in the present invention are RSs specified for respectivegroups, and are RSs multiplied by beamforming matrices, like an existingDM-RS (or UE-specific RSs of LTE) of LTE-Advanced. Here, respectivegroup beamforming vectors b_(g,i) (an column vector ofB_(g)=[b_(g,1),b_(g,2), . . . , b_(g,b′)]) are multiplied to generate b′GRSs. Needless to say, in case of s′<b′, only s′ GRSs may be selectedfrom among the b′ GRSs and generated to reduce RS overhead. In addition,GRSs of groups that belong to one class through the above-described UEgrouping and group beamforming matrix generation process slightlyinterfere with each other, and thus can use the same resources withoutinterfering with each other by additionally applying a pseudo-randomsequence, like a DM-RS.

A GRS performs both of CSI-RS and DM-RS functions of LTE as follows.

(1) CSI-RS Function

The CSI-RS function performed by a GRS is intended to enable pieces ofUE to feed back instantaneous channel information. In the presentinvention, UE can estimate h_(gk) ^(H)b_(g,i) through a GRS and performimplicit channel feedback such as a CQI, a PMI and RI on the basis ofh_(gk) ^(H)b_(g,i).

(2) DM-RS Function

The DM-RS function performed by a GRS is to implicitly transfer abeamforming (or precoding) vector selected by a base station to UE, andto become an RS for DM. A GRS can be regarded as a specific RS, whichwill be described later. In other words, the group beamforming matrixB_(g) provides beamforming vectors optimized for pieces of UE in agroup, and thus the base station has no reason to select a beamformingvector other than B_(g). Thus, it is all right for UE to regard that atransmission signal has been beamformed through a PMI fed back by the UEitself, and it is not required to additionally transmit a DM-RS to knowa beamforming vector selected by the base station. In addition, evenwhen scheduling has not been performed according to a rank fed back byUE, the corresponding PMI does not change, and the UE can know its rankand PMI through a simple detection test without the help of the basestation.

For example, when UE feeds back two CQIs and PMIs using rank 2, in orderto know how many ranks and which PMI are actually used for transmissionby a base station, the UE performs detection for each of three cases (acase in which a rank is rank 2 and thus both of PMI1 and PMI2 are used,a case in which a rank is rank 1 and PMI1 is used, and a case in which arank is rank 1 and PMI2 is used), and then can know a correct rank andPMI by calculating a post SINR.

It is apparent that a GRS can be an RS for coherent demodulation. Allgroups belonging to one class can reuse the same GRS resources, and atthis time, quasi-orthogonal sequences are used according to therespective groups. Also, pieces of UE scheduled for the same time in agroup are identified using different orthogonal sequences. For example,when four pieces of UE are scheduled in each group, an orthogonalsequence having a sequence length of 4 is necessary.

A GRS resource location can use a resource location of an existingDM-RS. Also, unlike in related art, resources of a GRS are not necessaryas many as the number of scheduled pieces of UE due to resource reusebetween groups, but are required as many as a number obtained bydividing the number of pieces of UE scheduled for the same time by thenumber of groups. For example, when 16 pieces of UE are classified intofour groups and scheduled for the same time, four GRS resources ratherthan 16 GRS resources are necessary (in case of assigning one layer perone piece of UE).

A case in which a GRS is used for the CSI-RS function has an advantageof higher periodicity. While an existing CSI-RS has a minimumtransmission period of 5 ms, a (fixed rather than minimum) period of aGRS can be 5 ms or less. This is because a GRS is transmitted in everysubframe to which resources of the corresponding class are allocated,like a DM-RS. In addition, while a DM-RS exists only when thecorresponding resources are allocated to UE, a GRS exists even whenanother piece of UE in the same group is allocated the correspondingresources. Thus, there is another advantage in that channel estimationcan be more accurately performed using a DM-RS even when UE is notallocated the corresponding resources.

Meanwhile, it is possible to separately prepare GRSs as a CSI-GRS and aDM-GRS according to the types of a CSI-RS and a DM-RS of LTE-Advanced.In this case, there is a problem that RS overhead is additionallyrequired. Also, when a DM-GRS is separately prepared, a base station cantransmit the DM-GRS using a precoding matrix other than a GBF vector fedback from UE, but the resultant benefit is determined to be verylimited.

E) UE Feedback

In this specification, implicit UE feedback will be mainly described,and explicit UE feedback will be simply described at the end.

1) Eigenvector Matrix

As described above, when it is difficult to estimate a transmitcorrelation matrix using an uplink SRS only, UE should estimate atransmit correlation matrix through a CSI-RS. Thus, the UE estimates thetransmit correlation matrix through the CSI-RS, and feeds backinformation on a dominant eigenvector matrix U_(g), of the transmitcorrelation matrix in the following two methods.

Explicitly feed back an estimated value of the eigenvector matrix U_(g),through vector quantization.

Extract an AS and an AoD from an estimated value of the eigenvectormatrix U_(gk), and implicitly feed back the AS and the AoD. A basestation receives feedback of an AS and an AoD, and can estimate U_(gk)under the assumption of the one-ring channel model. Here, using awell-verified super-resolution algorithm such as MUultiple SIgnalClassification (MUSIC) and Estimation of Signal Parameters viaRotational Invariance Technique (ESPRIT), it is possible to estimate theAS and the AoD (or angle of arrival (AoA)) from a channel matrix. Inthis specification, description of a detailed algorithm for extractingan AS and an AoD will be omitted.

2) Adaptive Codebook

As a codebook of implicit UE feedback for the present invention, anadaptive codebook rather than a fixed codebook of LTE can be used. Inother words, since beamforming vectors constituting the codebook arespecified for groups (equivalent to pieces of UE), the beamformingvectors are different according to the respective groups and classes andcan very slowly change according to time (movement andactivation/deactivation of UE).

The adaptive codebook of the present invention can be presented asEquation 14 below.

C={C ⁽¹⁾ ,C ⁽²⁾ , . . . C ^((T))}  [Equation 14]

Here, a codebook subset C^((t)) corresponding to class t is as follows.

C ^((t)) ={W ₁ ^((t)) ,W ₂ ^((t)) , . . . ,W _(G) ^((t))}  [Equation 15]

W_(g) ^((t)) of each class codebook C^((t)) consists of b′(=b/G)beamforming vectors b_(g,i) ^((t)). As mentioned above, the adaptivecodebook can be very slowly change according to time, and an indexindicating time is omitted.

Next, an adaptive codebook will be proposed according to a Rel. 10 dualcodebook appropriate for a cross-polarization antenna, and the requiredamount of PMI feedback resources will be calculated. When a rank of UEis 1 (or when a base station assigns only one layer to each piece ofUE), an adaptive codebook of class t and group g is as follows.

$\begin{matrix}{W_{g}^{(t)} \in \left\{ {{{\begin{bmatrix}b_{g,i}^{(t)} \\{\alpha \; b_{g,i}^{(t)}}\end{bmatrix}\text{:}\mspace{14mu} i} = 1},\cdots \mspace{14mu},{{b^{\prime}\mspace{14mu} {and}\mspace{14mu} \alpha} = 1},{- 1},j,{- j}} \right\}} & \left\lbrack {{Equation}\mspace{14mu} 16} \right\rbrack\end{matrix}$

Here, α is a co-phasing factor, and intended for coherent combining ofco-polarization antenna signals. Thus, in case of b′=4, PMI feedbackresources of four bits are necessary. Even when a rank is 2 or higher,it is possible to design an adaptive codebook with a very limitedcombination, like an LTE dual codebook, to maintain the required PMIresource amount of four bits. In this case, there is surely a tradeoffbetween system capacity and feedback load. A codebook of rank 2 orhigher should be designed through not only a logical basis but also morerealistic performance analysis, such as a system-level simulation (SLS),and thus will be omitted in the present conceptual design.

Unless imperatively necessary, a superscript t denoting a class will beomitted in all equations for convenience while the equations aredescribed in this specification.

3) Fixed Codebook

As a codebook of the present invention in another form, a fixed codebookof can be designed like in LTE. According to the above-describedadaptive codebook, assuming that a base station knows transmitcorrelation matrices of all pieces of UE, the base station performs UEgrouping, designs a codebook optimized for the pieces of UE currently ina cell, and transmits the codebook using a GRS, and the pieces of UEmeasure the codebook and feed back beamforming vectors best forthemselves. When the base station estimates the transmit correlationmatrix of the pieces of UE using SRSs only, the pieces of UE do not evenneed to know their transmit correlation matrices.

On the other hand, according to the fixed codebook, without assumingthat a base station knows transmit correlation matrices of all pieces ofUE, a predetermined limited codebook is designed, and UE first estimatesits transmit correlation matrix through CSI-RSs of considerably longperiods, selects a beamforming vector best for the UE itself on thebasis of the codebook, and feeds back the beamforming vector.

This codebook has a characteristic that it is designed to satisfysimilarity between UE and a specific group and orthogonality betweengroups.

For example, when ASs of UE are classified into four stages including 5degrees, 10 degrees, 20 degrees, and more than 20 degrees, the number ofgroups that can be formed in one sector having a range of 120 degrees(i.e., that are quasi-orthogonal) at each AS can be 4, 3, 2, or 1. Inother words, assuming that the AS is 5 degrees, four beamformingmatrices corresponding to four groups are generated in one sector. Amethod of forming a set of such beamforming matrices (i.e., a codebook)can be designed under the assumption of, for example, the one-ringchannel model.

Next, codebook design will be described. To design a good codebook, ameasure should be clearly defined. For example, in this specification, ameasure of orthogonality is defined as Equation 17 below.

$\begin{matrix}{\beta_{g,h}^{(t)} = {\frac{1}{\sqrt{r_{g}^{{(t)}^{*}}r_{h}^{{(t)}^{*}}}}{s\left( {U_{g}^{{(t)}^{*}},U_{h}^{{(t)}^{*}}} \right)}}} & \left\lbrack {{Equation}\mspace{14mu} 17} \right\rbrack\end{matrix}$

Here s(A,B)=∥A*B∥_(F) ², and the smaller the value, the higher theorthogonality between two groups g and h. In codebook design, it isimportant to carefully determine an AoD set Θ^((t)) of each group sothat eigenvectors U_(g) of groups become quasi-orthogonal to each other.According to a given AS and the number of groups, an optimum Θ^((t))that minimizes the value of Equation 17 can be calculated using Equation18 below.

$\begin{matrix}{\Theta^{{(t)}\overset{{^\circ}}{a}} = {{argmin}_{\Theta^{(t)}}{\sum\limits_{g = 1}^{G - 1}\; {\sum\limits_{h = {g + 1}}^{G}\; \beta_{g,h}^{(t)}}}}} & \left\lbrack {{Equation}\mspace{14mu} 18} \right\rbrack\end{matrix}$

A measure for calculating the optimum Θ^((t)) is to minimize the sum oforthogonalities between all groups of class t.

A Θ^((t)) design method in case of an AS being 10 degrees will bedescribed as an example according to the aforementioned measure. In thiscase, to determine three group AoDs, a reference angle or an anchorangle θ_(ref) of around 0 degree that becomes a reference is determined.In this case, a range of the reference angle becomes 40 degrees (120/3)by dividing 120 degrees of one sector by a number G of groups, and thereference angle has a value of −20 degrees to 20 degrees. When only anAoD of one case of the reference angle being 0 degree to 20 degrees iscalculated using a symmetry characteristic of Θ^((t)), an AoD of theother side also is easily calculated. When the range from 0 degree to 20degrees is divided by 16, 16 reference angles θ_(ref) are obtained. Whenone of the 16 reference angles is selected, the other two AoDs can begiven as Equation 19 below.

θ₁=θ_(ref)−40°+δ₁, θ₂=θ_(ref)+40°+δ₂  [Equation 19]

Here, δ₁, δ₂, each can have a range from −5 degrees to 5 degrees and agranularity of 1 degree. Thus, upon calculation of the AoD set Θ^((t)),the number of cases is about 1,936 (=16*11*11) or less at most.Considering an AoD of the other side in the same way, it is possible tocalculate a total of 32 fixed AoD reference angles and the other twoAoDs θ₁, θ₂ corresponding to each of the 32 fixed AoD reference angles.

Using AoD Θ^((t)) calculated from the given AS, the number oftransmitting antennas, distance between the antennas, and the equations,the eigenvector matrix U_(g) of each group can be calculated through theone-ring channel model. In general, U_(g) calculated in this way are notaccurately orthogonal to each other. Since the core of implementationaccording to the present invention is orthogonality between groups, itis necessary to improve orthogonality between groups through BD. Thus, abeamforming matrix B_(g) is calculated through BD of the matrices U_(g)calculated in advance.

Meanwhile, long-term PMI feedback caused by the above-described fixedcodebook is classified into the following two types. First, when an ASof UE is divided into four AS ranges, two bits are required. When eachAS is 10 degrees, the number of cases of the beamforming matrix B_(g)requires seven bits for the AS including 32 reference angles θ_(ref) and2-bit information indicating a belonging group among three groups. Sincethe AS and B_(g) are very slowly changing statistical characteristics,feedback of them has a very long period, or they may be fed back onlywhen there is a change in them.

4) MU-CQI

Unlike the above-described long-term UE feedback, this channelinformation feedback is instantaneous feedback. CQI feedback of LTE is aSU-CQI based on SU-MIMO. In other words, a CQI having no information oninterference caused by another piece of UE scheduled on the sameresources is fed back. On the other hand, it is well known that a MU-CQIin which interference caused by another piece of UE is taken intoconsideration is necessary in MU-MIMO, and benefit of the MU-CQI can bevery much. For this reason, in existing LTE, a MU-CQI is approximatelyestimated by, for example, calculating a predicted MU-CQI forimplementation, but the estimated MU-CQI may be significantly differentfrom an accurate MU-CQI. A GRS structure of the present inventionfacilitates MU-CQI feedback of UE. As mentioned above, UE g_(k) canestimate h_(gk) ^(H)b_(g,m), m=1, . . . , b′ that is an inner product ofbeamforming vectors of a group to which the UE belongs and a channelthrough a GRS. Assuming that a total of b′ beamforming vectors aresimultaneously transmitted, the UE measures a MU-CQI corresponding to anSINR as follows, and feeds back the MU-CQI to a base station.

$\begin{matrix}{{{MU} - {CQI}_{g_{k}}} = {\max\limits_{m}\frac{{{h_{g_{k}}^{H}b_{g,m}}}^{2}}{\sigma^{2} + {\sum_{{n = 1},{n \neq m}}^{b^{\prime}}{{h_{g_{k}}^{H}b_{g,n}}}^{2}}}}} & \left\lbrack {{Equation}\mspace{14mu} 20} \right\rbrack\end{matrix}$

Here, σ² denotes background noise and interference of other cells. Thebase station can cause only UE that measures a higher MU-CQI than aspecific reference value to perform feedback. The above equation isunder the assumption of rank 1 of co-polarization, and in case ofcross-polarization and rank 2 or higher, a post SINR in which areception algorithm such as minimum mean-square error (MMSE) detectionor turbo reception is taken into consideration should be calculated.

The MU-CQI is efficient because of a high probability that the basestation will simultaneously transmit all of the b′ beamforming vectors.This is cause b_(g,m) is a beamforming vector specified for pieces of UEbelonging to a group, and there are b′ candidate beams, which have avery smaller number than a fixed beam method (when M=8 like in LTE Rel.10, W1 consists of four beams, which are substantially eight beams incase of a cross-polarization antenna, and when M increases to 32, W1consists of 32 beams. On the other hand, since the present invention hasa characteristic that quasi-orthogonality between groups is maintained,b′=4 in general when M=32. Meanwhile, when the number of pieces of UEbelonging to a specific group is small, the base station can select s′beamforming vectors, which is less than b′ (s′<b′) among b′ beamformingvectors and simultaneously transmit the s′ beamforming vectors or limitbeamforming vectors to two or three specific combinations of

$\begin{pmatrix}b^{\prime} \\s^{\prime}\end{pmatrix}.$

In this case, there can be the following ideas.

The base station should notify UE of which s′ beams (s′ beams selectedfrom among b′ beams can vary over time) are used, or which combinationis used.

As mentioned in the adaptive codebook method among the UE feedbackmethods, a codebook is limited, like in LTE, so that only a fixedspecific combination is used.

The number of bits required for feeding back a MU-CQI can be the same asa value of an existing LTE SU-CQI.

Meanwhile, a PMI to be fed back by UE corresponds to an index m thatmaximizes Equation 20 above.

5) Group Interference Measurement

MU-CQI calculation of Equation 20 above is under the assumption thatinterference of other groups is very slight. Meanwhile, when there is asignificant disagreement between a beamforming vector B_(g) (i.e., U_(g)) of a group and an eigenvector U_(g) of UE g_(k), the corresponding UEmay encounter considerable interference from other groups. To solve sucha potential problem, it is possible to use the fact that, when aspecific beam becomes a strong interference signal, the corresponding|h_(gk) ^(H)b_(g′,n)| can be estimated because the UE can not onlyreceive a GRS of its group but also receive GRSs of all groups. At thistime, the UE needs to remove beams |h_(gk) ^(H)b_(g,m)|, m=1, . . . , b′of its group from the received signals and estimate |h_(gk)^(H)b_(g′,n)|. To this end, a base station should transmit a controlsignal for receiving GRSs of all groups to all pieces of UE belonging toa specific class, or make a GRS sequence determination formula in whicha group identifier (ID) as well as a cell ID are included.

When the UE measures and considers interference of other groups inMU-CQI calculation as described above, Equation 20 is replaced byEquation 21 below.

$\begin{matrix}{{{MU} - {CQI}_{g_{k}}} = {\max\limits_{m}\frac{{{h_{g_{k}}^{H}b_{g,m}}}^{2}}{\begin{matrix}{\sigma^{2} + {\sum_{,{n \neq m}}{{h_{g_{k}}^{H}b_{g,n}}}^{2}} +} \\{\sum_{g^{\prime} \neq g}{\sum_{n = 1}^{b^{\prime}}{{h_{g_{k}}^{H}b_{g^{\prime},n}}}^{2}}}\end{matrix}}}} & \left\lbrack {{Equation}\mspace{14mu} 21} \right\rbrack\end{matrix}$

Here, in Σ_(g′≠g)Σ_(n=1) ^(b′)|h_(gk) ^(H)b_(g′,n)|², only groupinterference exceeding a specific reference value is actually taken intoconsideration. Such a MU-CQI can be regarded as a safety factor for whenunexpected considerable group interference is in a MU-MIMO systemaccording to the present invention.

6) RI

As described above, the present invention uses adaptive beams optimizedfor MU-MIMO in a scenario in which there are a large number oftransmitting antennas and active pieces of UE, and thus a probabilitythat a base station will schedule all MIMO resources for MU-MIMO becomesvery high. In such MU-MIMO, the base station generally limits a rank ofUE to 1 or 2 to increase system capacity. On the other hand, when thereare a small number of pieces of UE, the base station may cause thepieces of UE to perform feedback using SU-CQIs to increase systemcapacity.

When a rank set by the base station is 2, UE feeds back CSIcorresponding to two code words, and thus the base station can seefeedback of pieces of UE belonging to each group and determine whetherto perform transmission to scheduled UE using actual rank 1 or 2.

7) Explicit UE Feedback

Explicit UE feedback is a method of feeding back direct information on achannel matrix instead of a PMI. Each piece of UE quantizes and feedsback its modified channel vector h_(gk) ^(H)B_(g) to a base station. Anexample of a method of quantizing a channel vector is a quantizationmethod using channel direction information (CDI).

The base station calculates a precoding matrix P_(g) according to analgorithm (e.g., zero-forcing (ZF) beamforming) using explicit channelfeedback of UE and CQI information obtained by measuring interference ofother cells, and performs MU-MIMO using the precoding matrix P_(g). Inthis case, a difference with the implicit UE feedback method is that aDM-RS is additionally necessary besides a GRS serving as a CSI-RS.

F) Downlink Control Signal

In addition to an existing downlink control signal of LTE-Advanced, anew control signal as described below is necessary. The control signalis about CSI measurement resources (i.e., a scheduling resourcecandidate).

A large-scale transmitting antenna system simultaneously accommodatesfar more active pieces of UE than an existing system. An LTE system hasa basic mode in which UE is caused to measure and report CSI on anentire frequency band. However, when the number of active pieces of UEbecomes very large, it may be difficult for the system to take such CSIfeedback overhead. To solve this problem, the present invention canapply a method of allocating CSI measurement resources according toclasses.

In an LTE-Advanced downlink MIMO transmission method, a base stationsends CSI measurement resources of UE using a radio resource control(RRC) message, and the information is not changed while the UE is in anactive state. On the other hand, in the present invention, pieces of UEare classified (pre-scheduled) according to classes, and thecorresponding class of UE can be changed according to movement of the UEor load balancing between classes.

In addition, since a base station allocates different resourcesaccording to classes, CSI measurement and scheduling resources can bechanged when a class of UE is changed while the UE is in the activestate. Unlike in LTE-Advanced, CSI measurement resources can be changedin the active state, and thus a CSI measurement resource control signalaccording to the present invention accords with characteristics of amedia access control (MAC) message rather than an RRC message.

FIG. 6 is a conceptual diagram of an example of allocation of CSImeasurement resource or scheduling resource candidates according to thepresent invention.

FIG. 6 is a case in which the number of classes is four. This shows anexample of a case in which separate resources are allocated according toclasses. For example, resources 601 filled with lower left-to-upperright diagonal lines may be allocated to class 1, and resources 603filled with upper left-to-lower right diagonal lines may be allocated toclass 3. The number of classes and an allocated resource size can varysemi-statically (about several minutes to tens of minutes) according todistribution of UE.

According to the present invention, a CSI measurement resource controlsignal can be signaled in the following two methods.

1) Case of Using Class ID (RNTI)

A base station transfers all class-specific CSI measurement resource orscheduling resource maps/modes to respective pieces of UE through MACmessages, and notifies the respective pieces of UE of their class radionetwork temporary identifiers (RNTIs). When a class of UE is changedalong with movement of the UE, etc., the base station only signals thechanged class RNTI using a MAC message, and the UE can know resources ofthe corresponding class. Also, using the class RNTI, it is possible toperform multicast according to classes and groups.

2) Case of Using No Class ID (RNTI)

A base station notifies pieces of UE of CSI measurement resources ofclasses corresponding to the respective pieces of UE using MAC messages,and notifies UE of CSI measurement resources of a class changed alongwith movement of the UE, or so on.

In case 1) above, UE requires a group ID (RNTI) to know its GRSsequence. The base station makes a GRS sequence determination formulaincluding a cell ID and a group ID, and thereby can cause the UE to knowthe GRS sequence using only a cell ID and a group ID. Since differentresources are used for respective classes, it is unnecessary to dividethe GRS sequence according to class IDs. Also, in the present invention,an adaptive codebook whereby a GRS is specified for UE is used, and thusa PMI is mapped to a GRS sequence in a one-to-one fashion. As describedabove regarding a GRS, UE can implicitly know its beamforming matrix inthe present invention. Thus, a GRS sequence needs not to be dynamicallyassigned through a physical downlink control channel (PDCCH), unlike anexisting DM-RS sequence.

Meanwhile, in the present invention, it may be unnecessary for a basestation to explicitly notify UE of RI through a PDCCH like a PMI. Thebase station determines the number of layers to be actually transmittedto the UE with reference to RI fed back by the UE, and transmits thelayers. The UE calculates a post-SINR through a process such as a simpledetection test for a PMI, and thereby can know the number of layersactually transmitted by the base station.

G) Scheduling

In the present invention, a group can be regarded as a virtual sectorbased on group classification. In other words, a base station mayperform scheduling separately according to respective classes andgroups. As described above regarding a MU-CQI, the base station receivesfeedback of MU-CQIs from all active pieces of UE belonging to a specificgroup. Scheduling performed by the base station is a process of findinga combination of pieces of UE that maximizes a utility function in whichthe MU-CQIs are multiplied by weights denoting the fairness of therespective pieces of UE. For example, even when some pieces of UE feedback the same beamforming vector (PMI) or a plurality of beamformingvectors, weights are multiplied according to the respective pieces of UE(when specific UE has a large amount of accumulated received data, thecorresponding weight generally decreases in proportion to the largeamount for the sake of fairness), and a combination of pieces of UE thatmaximizes the utility function may be found.

H) 3D Beamforming

Thus far, in this specification, beamforming in which a large-scaletransmitting antennas are arranged along a horizontal axis has beentaken into consideration. In addition to this, a large-scaletransmitting antenna system in which an antenna arrangement is extendedalong a vertical axis can also be taken into consideration, and abeamforming technique using both of horizontal-axis and vertical-axisspaces is referred to as three-dimensional (3D) beamforming.

FIG. 7 is a conceptual diagram of a 3D beamforming technique, in which amacro base station located in an urban skyscraper performs beamformingusing both of horizontal-axis and vertical-axis spaces.

This section introduces a 3D beamforming technique by which the conceptof MIMO transmission of the present invention in which the foregoinghorizontal-axis beamforming is taken into consideration is extended tothe vertical axis as well.

First, a CSI-RS structure and a PMI feedback scheme for 3D beamformingwill be described in brief.

In case of a long-term CSI-RS structure, even when a base station has aplurality of arrays along each of the horizontal axis and the verticalaxis, there can be one eigenvector matrix, which is a statisticalchannel characteristic of UE, along each of the horizontal axis and thevertical axis because the arrays are two-dimensional (2D) antennaarrays. Thus, a long-term CSI-RS needs not to be in every 2D antennaelement, and it is all right to transmit the long-term CSI-RS using onerow as the horizontal axis and one column as the vertical axis.

In case of a short-term CSI-RS structure, an eigenvector matrix of UEhas a structure as described above, but a short-term fading channel mayhave a plurality of different arrays along each of the horizontal axisand the vertical axis. Thus, a short-term CSI-RS needs to be transmittedfor every 2D antenna element.

In case of long-term PMI feedback, a long-term PMI consists of onelong-term PMI (a horizontal-axis class and a group ID) as the horizontalaxis and one long-term PMI (a vertical-axis class and a group ID) as thevertical axis according to a long-term CSI-RS.

In case of short-term PMI feedback, since short-term PMIs may varyaccording to all 2D antenna elements, a plurality of short-term PMIs arefed back along the horizontal axis and a plurality of short-term PMIsare fed back along the vertical axis.

1) Channel Model

A column (horizontal axis) size of a 2D antenna array is M, a row(vertical axis) size is N, and the drawing shows an example of 3Dbeamforming for one class.

In this specification, only one class is taken into consideration forconvenience, and it is assumed that active pieces of UE can be spatiallyclassified into L vertical groups along the vertical axis, and Ghorizontal groups along the horizontal axis. Transmit correlationmatrices of the vertical axis and the horizontal axis are R_(V,l) andR_(H,g), respectively. These vertical and horizontal transmitcorrelation matrices are presented through eigendecomposition asfollows.

R _(V,l) =U _(V,l)Λ_(V,l) U* _(V,l) , R _(H,g) =U _(H,g) Λ _(H,g) U*_(H,g)  [Equation 22]

In this case, when a Kronecker (more accurately, one-ring) channel modelis extended to a 3D channel model, the 3D correlation matrix R_(l,g) ispresented as a Kronecker product as follows.

R _(l,g) =R _(H,g) ⊗R _(V,l)=(U _(H,g) ⊗U_(V,l))(Λ_(H,g)⊗Λ_(V,l))(U*_(H,g) 237 U*_(V,l))  [Equation 23]

Using the 3D transmit correlation matrices, a channel vector of UEbelonging to vertical/horizontal groups g and 1 is presented as Equation24 below.

h* _(l,gk) =w* _(l,gk)(Λ_(H,g) ^(1/2)⊗Λ_(V,l) ^(1/2))(U* _(H,g) ⊗U_(V,l)*)  [Equation 24]

2) Beamforming Matrix

A 3D transmit vector is presented as Equation 25 below.

x=Bpd=(B _(H) ⊗B _(V))(P _(H) ⊗P _(V))d  [Equation 25]

Here, B_(H), B_(V), P_(H), and P_(v) are M×b_(H), N×b_(v), b_(H)×s_(H),b_(v)×s_(v) dimension matrices respectively, and d is an ^(SHSV)dimension data symbol. As shown in channel model Equation 24 andEquation 25 above, beamfoming/precoding of the horizontal axis can beperformed independently from beamfoming/precoding of the vertical axis.

(U* _(H,g) ⊗U* _(V,l))(B _(H) ⊗B _(V))=(U* _(H,g) B _(H))⊗(U* _(V,l) B_(V))  [Equation 26]

Using the relationship of Equation 26 above, a reception vector ofvertical/horizontal groups 1 and g is presented as Equation 27 below.

$\begin{matrix}\begin{matrix}{y_{t,g} = {{{W_{l,g}^{*}\left( {\Lambda_{H,g}^{1/2} \otimes \Lambda_{V,l}^{1/2}} \right)}\left( {U_{H,g}^{*} \otimes U_{V,l}^{*}} \right)\left( {B_{H} \otimes B_{V}} \right){Pd}} + z_{l,g}}} \\{= {{{W_{l,g}^{*}\left( {\Lambda_{H,g}^{1/2} \otimes \Lambda_{V,l}^{1/2}} \right)}\left( {{\left( {U_{H,g}^{*}B_{H}} \right) \otimes \left( {U_{V,l}^{*}B_{V}} \right)}{Pd}} \right)} + z_{l,g}}} \\{\approx {{W_{l,g}^{*}\left( {\Lambda_{H,g}^{1/2} \otimes \Lambda_{V,l}^{1/2}} \right)}\left( {\left( {U_{H,g}^{*}B_{H,g}} \right) \otimes} \right.}} \\{\left. {\left( {U_{V,l}^{*}B_{V,l}} \right)P_{l,g}d_{l,g}} \right) + z_{l,g}}\end{matrix} & \left\lbrack {{Equation}\mspace{14mu} 27} \right\rbrack\end{matrix}$

Here, B_(H)=diag(B_(H,l), . . . , B_(H,G)) and B_(V)=diag(B_(V,l), . . ., B_(V,L)). channel, when interference between differentvertical/horizontal groups 1 and g becomes insignificant due to UEgrouping, the following equation is satisfied, and an approximatelyequal sign in the above equation is valid.

U _(H,l,g) B _(H,l′,g′)≈0, U* _(V,l) B _(V,l′)≈0, for l′≠l,g′≠g  [Equation 28]

According to the above equation, an optimum 3D beamforming matrix is asfollows, like a 2D beamforming matrix according to the presentinvention.

B _(H,g) =U _(H,g) B _(V,l) =U _(V,l)  [Equation 29]

3) Codebook

Groups for 3D beamforming correspond to vertical/horizontal-axis groupsobtained by subdividing a (horizontal axis) group for 2D beamforming,and a codebook of class t can be presented to include the vertical axisas follows.

C ^((t)) {W _(1,1) ^((t)) , . . . , W _(1,L) ^((t)) , . . . , W _(G,1)^((t)) , . . . , W _(G,L) ^((t))}  [Equation 30]

As shown in Equation 25, a 3D beamforming matrix is presented asfollows.

$\begin{matrix}\begin{matrix}{{B_{H} \otimes B_{V}} = {\begin{bmatrix}B_{H,1} & \cdots & 0 \\\vdots & \ddots & \vdots \\0 & \cdots & B_{H,G}\end{bmatrix} \otimes \begin{bmatrix}B_{V,1} & \cdots & 0 \\\vdots & \ddots & \vdots \\0 & \cdots & B_{V,L}\end{bmatrix}}} \\{= {\quad\begin{bmatrix}{B_{H,1} \otimes B_{V,1}} & \cdots & 0 \\\vdots & \ddots & \vdots \\0 & \cdots & {B_{H,G} \otimes B_{V,L}}\end{bmatrix}}}\end{matrix} & \left\lbrack {{Equation}\mspace{14mu} 31} \right\rbrack\end{matrix}$

Here, B_(H,g), B_(V,l) are given by Equation 29. Thus, in case of rank 1transmission to a cross-polarization antenna, W_(g,l) ^((t)) in Equation30 is as follows, like in Equation 16.

                                     [Equation  32]$W_{g,l}^{(t)} \in \left\{ {{{\begin{bmatrix}{b_{H,g,i}^{(t)} \otimes b_{V,g,h}^{(t)}} \\{\alpha \; {b_{g,i}^{(t)} \otimes b_{V,g,h}^{(t)}}}\end{bmatrix}\text{:}\mspace{14mu} i} = 1},\cdots \mspace{14mu},b_{H}^{\prime},{h = 1},\cdots \mspace{14mu},{{b_{V}^{\prime}\mspace{14mu} {and}\mspace{14mu} \alpha} = 1},{- 1},j,{- j}} \right\}$

For example, when b′_(H)=b_(H)/G=4, b′_(V)=b_(V)/L=2, the PMI ispresented using five bits. As described in 2D beamforming, in case ofrank 2 or higher, a limited combination selected from among all possiblecombinations should be determined.

Lastly, UE grouping, GRS, UE feedback, etc. are naturally extended tothe same concepts as in 2D beamforming, and will not be specified inthis specification.

FDD-Based Uplink MIMO Transmission and Reception Method According toPresent Invention

Extension of the above-described concept of FDD-based downlink MIMOtransmission to uplink MU-MIMO transmission will be described below.

A) Uplink MIMO Reception Signal

It is assumed below that all pieces of UE have N multiple antennas, andeach transmit Ns data streams for convenience. In this case, in uplinkMU-MIMO, a received signal of a base station is as follows.

$\begin{matrix}{v = {{\sum\limits_{i = 1}^{s}\; {H_{i}P_{i}u_{i}}} + z}} & \left\lbrack {{Equation}\mspace{14mu} 33} \right\rbrack\end{matrix}$

Here, P_(i) is an N×Ns dimensional precoding matrix, and μ_(i) is an Nsdimensional data symbol vector of an i^(th) piece of UE. In this uplinksystem, s denotes the number of pieces of UE scheduled by the basestation.

B) Characteristics of Uplink MIMO

The FDD-based downlink large-scale antenna MU-MIMO system describedabove has the problem of RS and CSI feedback, and the present inventionis mainly intended to solve the problem. Meanwhile, unlike the downlinklarge-scale antenna MU-MIMO system, an uplink large-scale antennaMU-MIMO system has the following problems.

1) Complexity of System Calculation

As mentioned above, a large-scale antenna system can solve the problemof overload on a system crowded by a large number of users at peak time.This means a case in which data transmission and reception activities ofusers are very active compared to an existing system and user populationin a cell. For this reason, a major problem of an uplink large-scaleantenna MU-MIMO system is calculation complexity.

In uplink MU-MIMO, a base station can obtain a much better estimated CSIvalue than downlink MU-MIMO through an SRS of UE, and a receiving endcan perform MU-MIMO through the estimated CSI value.

h _(i) ^(H) h _(j)≠0, i≠j  [Equation 34]

When orthogonality between instantaneous channels of two or more piecesof UE is ensured in existing uplink MU-MIMO, that is, when Equation 34above is satisfied, the pieces of UE are scheduled through MU-MIMO.

In this case, to calculate orthogonality between instantaneous channelsof all active pieces of UE, the base station should calculate an innerproduct of an M-dimensional vector

$\quad\begin{pmatrix}K \\2\end{pmatrix}$

times. Thus, when M and K are large as in a large-scale antenna system,calculation complexity becomes excessively high. Also, it is difficultto use a detection algorithm such as MMSE detection other than maximalratio combining (MRC) so as to obtain better performance.

Thus, the FDD-based uplink MIMO transmission and reception technologyaccording to the present invention is intended to perform uplink MU-MIMOwhile maintaining calculation complexity of a system to a practicablelevel. 2) Preservation of Orthogonality of Uplink/downlink ChannelCorrelation Matrices

In extension of the concept of the downlink MIMO transmission andreception method according to the present invention to the uplinkMU-MIMO transmission and reception method, it will be very useful thatpieces of UE classified into one group in a downlink are also classifiedinto one group in an uplink to perform MU-MIMO, or vice versa. This isbecause it is possible to reduce calculation complexity of a basestation for UE grouping to the half. As a result, orthogonality betweentransmit correlation matrices in a downlink can be preserved asorthogonality between receive correlation matrices in an uplink, whichcan be described through reference literatures including “3GPP RANIcontribution, R1-092024, Ericsson, 2009” and so on.

C) Precoding Matrix

A large-scale transmitting antenna MIMO system denotes that an antennaof a base station may be very larger than that of an existing system.Thus, the downlink MU-MIMO transmission method needs various changes asdescribed in the previous chapter. On the other hand, a row dimension ofan uplink precoding matrix is limited by a number N of antennas of UE,and N is generally limited to two to four according to a limitation onthe physical size of the UE. Also, since in uplink MU-MIMO, a basestation determines a precoding matrix through autonomous calculation andsignals the precoding matrix to UE, the uplink MU-MIMO is the same as anuplink MU-MIMO method of an existing LTE system, and the same precodingmatrix may be used.

The uplink MIMO transmission and reception method causes the basestation to have a reception signal vector μ^(g) of a group as shownbelow through uplink group classification like downlink groupclassification, or inter-group orthogonality preservation of theprevious paragraph.

$\begin{matrix}{{B_{g}^{UL}v} = {v_{g} \approx {{\sum\limits_{i = 1}^{s^{\prime}}\; {{\overset{\sim}{H}}_{g_{i}}P_{g_{i}}u_{g_{i}}}} + z}}} & \left\lbrack {{Equation}\mspace{14mu} 35} \right\rbrack\end{matrix}$

Here, s′ denotes the number of scheduled pieces of UE among pieces of UEin group g, and {tilde over (H)}_(gi)=B_(g) ^(UL)H_(gi) is a modifiedb′×N dimensional channel matrix. In case of a large-scale antennasystem, b′<<M, and the calculation complexity of a system can bereduced.

Meanwhile, an uplink scheduler of the base station calculates an SINR ofEquation 20 above to select a precoding matrix.

TDD-Based MIMO Transmission and Reception Method According to PresentInvention

A) TDD Downlink MIMO Transmission and Reception Method

A basic MIMO operation procedure of a time-division duplex (TDD) systemis as follows.

(1) A base station obtains downlink channel matrix information using anuplink SRS.

(2) UE calculates and reports a CQI through a CRS or a CSI-RS.

(3) The base station determines a precoding matrix, transmits a DM-RS,and signals scheduling information.

The TDD-based downlink MIMO transmission and reception technique and theFDD-based downlink MIMO transmission and reception technique arebasically the same, except whether a base station can obtain channelinformation using uplink/downlink channel reciprocity.

In this specification, only a portion of the TDD-based downlink MIMOtransmission and reception method different from the FDD-based downlinkMIMO transmission and reception method will be described on the basis ofa LTE TDD scheme.

1) DM-RS

The FDD-based downlink MIMO transmission and reception method accordingto the present invention is a MU-MIMO method based on implicit channelinformation feedback, and a codebook according to the FDD-based downlinkMIMO transmission and reception method is based on a GRS.

On the other hand, since a base station can know a relatively accuratechannel matrix in TDD, no codebook is necessary, and the base stationdetermines a beamforming matrix B_(g) and a precoding matrix P_(g).Thus, an existing DM-RS is necessary, unlike in an FDD scheme. A majordifference from a DM-RS of LTE is that in the present invention, Ggroups share DM-RS resources through UE grouping and beamforming tomaintain realistic DM-RS overhead.

2) CQI Feedback

In the TDD scheme, a CQI serves to measure interference of other cellsand background noise and cause UE to report the interference andbackground noise to a base station. Thus, the CQI has differentcharacteristics from a CQI for determining an actual modulation andcoding scheme (MCS) on the basis of a codebook as in the above-describedFDD scheme.

3) Precoding Matrix

In the TDD-based downlink MIMO transmission and reception method, a basestation performs UE grouping using a transmit correlation matrix of achannel matrix to generate beamforming matrices B_(g) according togroups, and generates modified channel vectors h_(gk) ^(H)B_(g)according to pieces of UE of each group. Using the channel vectors, itis possible to perform precoding for pieces of UE belonging to eachgroup according to an algorithm.

For example, ZF beamforming can be used, and for pieces of multi-antennaUE, a BD or block triangularization (BT) algorithm can be used.

B) TDD Uplink MIMO Transmission and Reception Method

There is no difference between a TDD-based uplink MIMO transmission andreception method and the FDD-based uplink MIMO transmission andreception method. Thus, it will be all right to refer to the FDD-baseduplink MIMO transmission and reception method described above.

While the invention has been shown and described with reference tocertain exemplary embodiments thereof, it will be understood by thoseskilled in the art that various changes in form and details may be madetherein without departing from the spirit and scope of the invention asdefined by the appended claims.

1. An operation method of a user equipment (UE) in a wirelesscommunication system, the operation method comprising: receiving, from abase station, information on a number of antennas and a subset of beamswhich is selected among all possible beams of the base station;generating a codebook based on the number of antennas included in theinformation; receiving a reference signal (RS) from the base station;determining channel state information (CSI) based on the RS and thesubset of beams by using the codebook; and reporting the CSI to the basestation, wherein the codebook is generated by using Kronecker product ofa first beamforming vector and a second beamforming vector.
 2. Theoperation method of claim 1, wherein the codebook is generated based onfollowing equation: $W_{g,l}^{(t)} \in \left\{ {{{\begin{bmatrix}{b_{H,g,i}^{(t)} \otimes b_{V,g,h}^{(t)}} \\{\alpha \; {b_{g,i}^{(t)} \otimes b_{V,g,h}^{(t)}}}\end{bmatrix}\text{:}\mspace{14mu} i} = 1},\cdots \mspace{14mu},b_{H}^{\prime},{h = 1},\cdots \mspace{14mu},{{b_{V}^{\prime}\mspace{14mu} {and}\mspace{14mu} \alpha} = 1},{- 1},j,{- j}} \right\}$wherein: b_(H,g,l) ^((t)) is the first beamforming vector, b_(V,g,h)^((f)) is the second beamforming vector, α is a co-phasing factor, i isan index of the first beamforming vector, h is an index of the secondbeamforming vector, b′_(H) is the number of first beamforming vectors,b′_(V) is the number of second beamforming vectors, and wherein each ofthe b′_(H) and the b′_(V) is determined based on the information on thenumber of antennas and the subset of beams.
 3. The operation method ofclaim 1, wherein the CSI includes at least one of a precoding matrixindicator (PMI), a channel quality indicator (CQI), and a rank indicator(RI).